SoroushVahidi/scidocs huggingface dataset:
Robust camera calibration of soccer video using genetic algorithm
(2008)

This paper proposes an exact and robust genetic algorithm-based method for the calibration of soccer camera. According to the FIFA official soccer field layout we defined a field model for the soccer court. Camera calibration is done through finding the homography transform between the field model and the input frame which is followed with DLT decomposition. The intersection of lines in the field model and input frame form feature points and by means of a genetic algorithm, we found the correspondence between those points. Our algorithm was applied to a couple of soccer video frames and the achieved results demonstrate its robustness, accuracy and high performance.
You are what you include: large-scale evaluation of remote javascript inclusions
(2012)

JavaScript is used by web developers to enhance the interactivity of their sites, offload work to the users' browsers and improve their sites' responsiveness and user-friendliness, making web pages feel and behave like traditional desktop applications. An important feature of JavaScript, is the ability to combine multiple libraries from local and remote sources into the same page, under the same namespace. While this enables the creation of more advanced web applications, it also allows for a malicious JavaScript provider to steal data from other scripts and from the page itself. Today, when developers include remote JavaScript libraries, they trust that the remote providers will not abuse the power bestowed upon them.
 In this paper, we report on a large-scale crawl of more than three million pages of the top 10,000 Alexa sites, and identify the trust relationships of these sites with their library providers. We show the evolution of JavaScript inclusions over time and develop a set of metrics in order to assess the maintenance-quality of each JavaScript provider, showing that in some cases, top Internet sites trust remote providers that could be successfully compromised by determined attackers and subsequently serve malicious JavaScript. In this process, we identify four, previously unknown, types of vulnerabilities that attackers could use to attack popular web sites. Lastly, we review some proposed ways of protecting a web application from malicious remote scripts and show that some of them may not be as effective as previously thought.
Memory engram storage and retrieval
(2015)

A great deal of experimental investment is directed towards questions regarding the mechanisms of memory storage. Such studies have traditionally been restricted to investigation of the anatomical structures, physiological processes, and molecular pathways necessary for the capacity of memory storage, and have avoided the question of how individual memories are stored in the brain. Memory engram technology allows the labeling and subsequent manipulation of components of specific memory engrams in particular brain regions, and it has been established that cell ensembles labeled by this method are both sufficient and necessary for memory recall. Recent research has employed this technology to probe fundamental questions of memory consolidation, differentiating between mechanisms of memory retrieval from the true neurobiology of memory storage.
Dimension Reduction and Data Visualization Using Neural Networks
(2007)

The problem of visual presentation of multidimensional data is discussed. The projection methods for dimension reduction are reviewed. The chapter deals with the artificial neural networks that may be used for reducing dimension and data visualization, too. The stress is put on combining the selforganizing map (SOM) and Sammon mapping and on the neural network for Sammon’s mapping SAMANN. Large scale applications are discussed: environmental data analysis, statistical analysis of curricula, comparison of schools, analysis of the economic and social conditions of countries, analysis of data on the fundus of eyes and analysis of physiological data on men’s health.
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
(2015)

The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.
Adaptive bootstrapping of recommender systems using decision trees
(2011)

Recommender systems perform much better on users for which they have more information. This gives rise to a problem of satisfying users new to a system. The problem is even more acute considering that some of these hard to profile new users judge the unfamiliar system by its ability to immediately provide them with satisfying recommendations, and may quickly abandon the system when disappointed. Rapid profiling of new users by a recommender system is often achieved through a bootstrapping process - a kind of an initial interview - that elicits users to provide their opinions on certain carefully chosen items or categories. The elicitation process becomes particularly effective when adapted to users' responses, making best use of users' time by dynamically modifying the questions to improve the evolving profile. In particular, we advocate a specialized version of decision trees as the most appropriate tool for this task. We detail an efficient tree learning algorithm, specifically tailored to the unique properties of the problem. Several extensions to the tree construction are also introduced, which enhance the efficiency and utility of the method. We implemented our methods within a movie recommendation service. The experimental study delivered encouraging results, with the tree-based bootstrapping process significantly outperforming previous approaches.
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Point Clouds
(2018)

The ability to segment unknown objects in depth images has potential to enhance robot skills in grasping and object tracking. Recent computer vision research has demonstrated that Mask R-CNN can be trained to segment specific categories of objects in RGB images when massive handlabeled datasets are available. As generating these datasets is time-consuming, we instead train with synthetic depth images. Many robots now use depth sensors, and recent results suggest training on synthetic depth data can generalize well to the real world. We present a method for automated dataset generation and rapidly generate a training dataset of 50k depth images and 320k object masks synthetically using simulated scenes of 3D CAD models. We train a variant of Mask R-CNN on the generated dataset to perform category-agnostic instance segmentation without hand-labeled data. We evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution images of challenging, denselycluttered bins containing objects with highly-varied geometry. SD Mask R-CNN outperforms point cloud clustering baselines by an absolute 15% in Average Precision and 20% in Average Recall, and achieves performance levels similar to a Mask RCNN trained on a massive, hand-labeled RGB dataset and fine-tuned on real images from the experimental setup. The network also generalizes well to a lower-resolution depth sensor. We deploy the model in an instance-specific grasping pipeline to demonstrate its usefulness in a robotics application. Code, the synthetic training dataset, and supplementary material are available at https://bit.ly/2letCuE.
Disruption of Large-Scale Brain Systems in Advanced Aging
(2007)

Cognitive decline is commonly observed in advanced aging even in the absence of disease. Here we explore the possibility that normal aging is accompanied by disruptive alterations in the coordination of large-scale brain systems that support high-level cognition. In 93 adults aged 18 to 93, we demonstrate that aging is characterized by marked reductions in normally present functional correlations within two higher-order brain systems. Anterior to posterior components within the default network were most severely disrupted with age. Furthermore, correlation reductions were severe in older adults free from Alzheimer's disease (AD) pathology as determined by amyloid imaging, suggesting that functional disruptions were not the result of AD. Instead, reduced correlations were associated with disruptions in white matter integrity and poor cognitive performance across a range of domains. These results suggest that cognitive decline in normal aging arises from functional disruption in the coordination of large-scale brain systems that support cognition.
Use of physiological signals to predict cybersickness
(2016)

http://dx.doi.org/10.1016/j.displa.2016.07.002 0141-9382/ 2016 Elsevier B.V. All rights reserved. q This paper was recommended for publication by Richard H.Y. So. ⇑ Corresponding author at: University of California, Irvine, 2201 Social & Behavioral Sciences Gateway Building, Irvine, CA 92697-5100, USA. E-mail addresses: mdenniso@uci.edu (M.S. Dennison), awisti@uci.edu (A.Z. Wisti), mdzmura@uci.edu (M. D’Zmura). Mark S. Dennison ⇑, A. Zachary Wisti, Michael D’Zmura
Secure Fine-Grained Access Control and Data Sharing for Dynamic Groups in the Cloud
(2018)

Cloud computing is an emerging computing paradigm that enables users to store their data in a cloud server to enjoy scalable and on-demand services. Nevertheless, it also brings many security issues, since cloud service providers (CSPs) are not in the same trusted domain as users. To protect data privacy against untrusted CSPs, existing solutions apply cryptographic methods (e.g., encryption mechanisms) and provide decryption keys only to authorized users. However, sharing cloud data among authorized users at a fine-grained level is still a challenging issue, especially when dealing with dynamic user groups. In this paper, we propose a secure and efficient fine-grained access control and data sharing scheme for dynamic user groups by: 1) defining and enforcing access policies based on the attributes of the data; 2) permitting the key generation center to efficiently update user credentials for dynamic user groups; and 3) allowing some expensive computation tasks to be performed by untrusted CSPs without requiring any delegation key. Specifically, we first design an efficient revocable attribute-based encryption (ABE) scheme with the property of ciphertext delegation by exploiting and uniquely combining techniques of identity-based encryption, ABE, subset-cover framework, and ciphertext encoding mechanism. We then present a fine-grained access control and data sharing system for on-demand services with dynamic user groups in the cloud. The experimental data show that our proposed scheme is more efficient and scalable than the state-of-the-art solution.
Machine Learning-Based Software Quality Prediction Models: State of the Art
(2013)

Quantification of parameters affecting the software quality is one of the important aspects of research in the field of software engineering. In this paper, we present a comprehensive literature survey of prominent quality molding studies. The survey addresses two views: (1) quantification of parameters affecting the software quality; and (2) using machine learning techniques in predicting the software quality. The paper concludes that, model transparency is a common shortcoming to all the surveyed studies.
Design and Implementation of Energy Management System With Fuzzy Control for DC Microgrid Systems
(2013)

This paper presents the design and implementation of an energy management system (EMS) with fuzzy control for a dc microgrid system. Modeling, analysis, and control of distributed power sources and energy storage devices with MATLAB/Simulink are proposed, and the integrated monitoring EMS is implemented with LabVIEW. To improve the life cycle of the battery, fuzzy control manages the desired state of charge. The RS-485/ZigBee network has been designed to control the operating mode and to monitor the values of all subsystems in the dc microgrid system.
3-Level high power converter with press pack IGBT
(2007)

Converteam recently has introduced the MV7000 series of medium voltage drives, designed with Press Pack IGBT (PPI) in 3-level NPC topology in the power range from 4 MW to 32 MW [3]. PPI combines the benefits of disk type device technology with state-of-the-art semiconductor technology. This paper explains relevant technical features and design issues related to MV7000 including direct series connection of devices.
Bayesian Unsupervised Topic Segmentation
(2008)

This paper describes a novel Bayesian approach to unsupervised topic segmentation. Unsupervised systems for this task are driven by lexical cohesion: the tendency of wellformed segments to induce a compact and consistent lexical distribution. We show that lexical cohesion can be placed in a Bayesian context by modeling the words in each topic segment as draws from a multinomial language model associated with the segment; maximizing the observation likelihood in such a model yields a lexically-cohesive segmentation. This contrasts with previous approaches, which relied on hand-crafted cohesion metrics. The Bayesian framework provides a principled way to incorporate additional features such as cue phrases, a powerful indicator of discourse structure that has not been previously used in unsupervised segmentation systems. Our model yields consistent improvements over an array of state-of-the-art systems on both text and speech datasets. We also show that both an entropy-based analysis and a well-known previous technique can be derived as special cases of the Bayesian framework.1
Block diagonalization for multi-user MIMO with other-cell interference
(2008)

Block diagonalization is one approach for linear preceding in the multiple-input multiple-output broadcast channel that sends multiple interference free data streams to different users in the same cell. Unfortunately, block diagonalization neglects other-cell interference (OCI), which limits the performance of users at the edge of the cell. This paper presents an OCI-aware enhancement to block diagonalization that uses a whitening filter for interference suppression at the receiver and a novel precoder using the interference-plus-noise covariance matrix for each user at the transmitter. For complex Gaussian matrix channels, the asymptotic sum rate of the proposed system is analyzed under a large antenna assumption for isotropic inputs and compared to conventional block diagonalization. The capacity loss due to OCI is quantified in terms of results from single-user MIMO capacity. Several numerical examples compare achievable sum rates, the proposed asymptotic rates, and the capacity loss, in low and high interference regimes.
Design and deployment of Aqua monitoring system using wireless sensor networks and IAR-Kick
(2014)

In Aquaculture, the yields (shrimp, fish etc.) depend on the water characteristics of the aquaculture pond. For maximizing fish yields, the parameters which are to be kept at certain optimal levels in water. The parameters can vary a lot during the period of a day and can rapidly change depending on the external environmental conditions. Hence it is necessary to monitor these parameters with high frequency . Wireless sensor networks are used to monitor aqua farms for relevant parameters this system consists of two modules which are transmitter station and receiver station. The data transmits through GSM to the Database at receiver station. The graphical user interface was designed, to convey the data in the form of a message to the farmers in their respective local languages to their Mobile Phones and alerts them in unhygienic environmental conditions, in order to take suitable actions. Keywords; aquaculture; wireless sensor networks; IAR-Kick;pH;
A Comparison over Focused Web Crawling Strategies
(2011)

In this paper we review and compare focused crawling strategies, studied and published during the past decade. Despite giant leaps in communication, storage and computing power in recent years, crawlers have always struggled to keep up with Web content generation and modification. Focused crawlers attempt to i) accelerate the crawling process, ii) maximize the harvest of high quality pages, iii) assign appropriate credit to different documents along a crawling path, such that short-term gains are not pursued at the expense of less obvious paths that ultimately yield larger sets of valuable pages. Beyond the review and comparison of the focused crawling strategies, we additionally propose additions to the corresponding architectures for further research.
Penetration Testing with Improved Input Vector Identification
(2009)

Penetration testing is widely used to help ensure the security of web applications. It discovers vulnerabilities by simulating attacks from malicious users on a target application. Identifying the input vectors of a web application and checking the results of an attack are important parts of penetration testing, as they indicate where an attack could be introduced and whether an attempted attack was successful. Current techniques for identifying input vectors and checking attack results are typically ad-hoc and incomplete, which can cause parts of an application to be untested and leave vulnerabilities undiscovered. In this paper, we propose a new approach to penetration testing that addresses these limitations by leveraging two recently-developed analysis techniques. The first is used to identify a web application's possible input vectors, and the second is used to automatically check whether an attack resulted in an injection. To empirically evaluate our approach, we compare it against a state-of-the-art, alternative technique. Our results show that our approach performs a more thorough penetration testing and leads to the discovery of more vulnerabilities.
Crawling AJAX by Inferring User Interface State Changes
(2008)

AJAX is a very promising approach for improving rich interactivity and responsiveness of web applications. At the same time, AJAX techniques shatter the metaphor of a web "page" upon which general search crawlers are based. This paper describes a novel technique for crawling AJAX applications through dynamic analysis and reconstruction of user interface state changes. Our method dynamically infers a state-flow graph modeling the various navigation paths and states within an AJAX application. This reconstructed model can be used to generate linked static pages. These pages could be used to expose AJAX sites to general search engines. Moreover, we believe that the crawling techniques that are part of our solution have other applications, such as within general search engines, accessibility improvements, or in automatically exercising all user interface elements and conducting state-based testing of AJAX applications. We present our open source tool called CRAWLJAX which implements the concepts discussed in this paper. Additionally, we report a case study in which we apply our approach to a number of representative AJAX applications and elaborate on the obtained results.
Flow-Sensitive Type Qualifiers
(2002)

We present a system for extending standard type systems with flow-sensitive type qualifiers. Users annotate their programs with type qualifiers, and inference checks that the annotations are correct. In our system only the type qualifiers are modeled flow-sensitively---the underlying standard types are unchanged, which allows us to obtain an efficient constraint-based inference algorithm that integrates flow-insensitive alias analysis, effect inference, and ideas from linear type systems to support strong updates. We demonstrate the usefulness of flow-sensitive type qualifiers by finding a number of new locking bugs in the Linux kernel.
A Comparison of Push and Pull Techniques for AJAX
(2007)

AJAX applications are designed to have high user interactivity and low user-perceived latency. Real-time dynamic Web data such as news headlines, stock tickers, and auction updates need to be propagated to the users as soon as possible. However, AJAX still suffers from the limitations of the Web's request/response architecture which prevents servers from pushing real-time dynamic web data. Such applications usually use a pull style to obtain the latest updates, where the client actively requests the changes based on a predefined interval. It is possible to overcome this limitation by adopting a push style of interaction where the server broadcasts data when a change occurs on the server side. Both these options have their own trade-offs. This paper explores the fundamental limits of browser-based applications and analyzes push solutions for AJAX technology. It also shows the results of an empirical study comparing push and pull.
Tractable Queries for Lightweight Description Logics
(2013)

It is a classic result in database theory that conjunctive query (CQ) answering, which is NP-complete in general, is feasible in polynomial time when restricted to acyclic queries. Subsequent results identified more general structural properties of CQs (like bounded treewidth) which ensure tractable query evaluation. In this paper, we lift these tractability results to knowledge bases formulated in the lightweight description logics DL-Lite and ELH. The proof exploits known properties of query matches in these logics and involves a querydependent modification of the data. To obtain a more practical approach, we propose a concrete polynomial-time algorithm for answering acyclic CQs based on rewriting queries into datalog programs. A preliminary evaluation suggests the interest of our approach for handling large acyclic CQs.
Twitter Sentiment Classification on Sanders Data using Hybrid Approach
(2015)

Sentiment analysis is very perplexing and massive issue in the field of social data mining. Twitter is one of the mostly used social media where people discuss on various issues in a dense way. The tweets about a particular topic give peoples’ views, opinions, orientations, inclinations about that topic. In this work, we have used pre-labeled (with positive, negative and neutral opinion) tweets on particular topics for sentiment classification. Opinion score of each tweet is calculated using feature vectors. These opinion score is used to classify the tweets into positive, negative and neutral classes. Then using various machine learning classifiers the accuracy of predicted classification with respect to actual classification is being calculated and compared using supervised learning model. Along with building a sentiment classification model, analysis of tweets is being carried out by visualizing the wordcloud of tweets using R.